{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"e2f274b4-20b8-5ad6-9162-b9438358a765","task_key":"default--v0~2e1~2e0~5fhf--e2f274b4-20b8-5ad6-9162-b9438358a765","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf e2f274b4-20b8-5ad6-9162-b9438358a765","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from matplotlib import pyplot as plt\\nfrom sklearn.decomposition import PCA\\ndef task_func(arr):\\n\",\"complete_prompt\":\"from matplotlib import pyplot as plt\\nfrom sklearn.decomposition import PCA\\n\\n\\ndef task_func(arr):\\n    \\\"\\\"\\\"\\n    Performs Principal Component Analysis (PCA) on the sum of rows of a 2D numpy array and plots the explained variance ratio.\\n\\n    Note:\\n    - The title of the plot is set to \\\"Explained Variance Ratio of Principal Components\\\".\\n\\n    Parameters:\\n    - arr (numpy.ndarray): A 2D numpy array. The input data for PCA.\\n\\n    Returns:\\n    - ax (matplotlib.axes.Axes): An Axes object from matplotlib.\\n\\n    Requirements:\\n    - scikit-learn\\n    - matplotlib\\n\\n    Notes:\\n    - The function assumes that 'arr' is a valid 2D numpy array.\\n    - Only the first principal component is considered in this analysis.\\n    - The plot illustrates the proportion of the dataset's variance that lies along the axis of this first principal component.\\n    \\n    Example:\\n    >>> import numpy as np\\n    >>> arr = np.array([[i+j for i in range(3)] for j in range(5)])\\n    >>> axes = task_func(arr)\\n    >>> axes.get_title()\\n    'Explained Variance Ratio of Principal Components'\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Performs Principal Component Analysis (PCA) on the sum of rows of a 2D numpy array and plots the explained variance ratio.\\nNote that: The title of the plot is set to \\\"Explained Variance Ratio of Principal Components\\\". Notes: The function assumes that 'arr' is a valid 2D numpy array. Only the first principal component is considered in this analysis. The plot illustrates the proportion of the dataset's variance that lies along the axis of this first principal component.\\nThe function should output with:\\n    ax (matplotlib.axes.Axes): An Axes object from matplotlib.\\nYou should write self-contained code starting with:\\n```\\nfrom matplotlib import pyplot as plt\\nfrom sklearn.decomposition import PCA\\ndef task_func(arr):\\n```\",\"libs\":\"['matplotlib', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}